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Autonomous systems encounter navigation challenges in GPS-denied environment, where GPS signals are unavailable or degraded due to obstruction, jamming, or spoofing. This paper presents a navigation framework that combines hyperspectral semantic data with an A-PNT uncertainty model to support navigation when GPS is unavailable. Principal Component Analysis (PCA) is first applied to reduce the dimensionality of the hyperspectral imagery while preserving important spectral information. The reduced data are then classified into terrain types, including roads, vegetation, water, and buildings, thereby creating a semantic map. Road extraction and skeletonization are used to identify preferred travel paths. A terrain-aware cost map is generated and updated according to the uncertainty of the A-PNT position estimate. An A* algorithm computes the route, while a replanning module adjusts the route when off-road segments are detected. A Kalman filter is applied to smooth the trajectory and improve navigation stability. The combination of 5 principal components and 6 clusters provided the best balance between classification quality and navigation performance, achieving 99% variance retention, full street coverage, and a planning time of 1.4 s. The proposed framework provides a practical solution for autonomous systems operating in GPS-denied environment.
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Security of GPS/INS Based On-road Location Tracking Systems
Location information is critical to a wide variety of navigation and tracking applications. GPS, today's de-facto outdoor localization system has been shown to be vulnerable to signal spoofing attacks. Inertial Navigation Systems (INS) are emerging as a popular complementary system, especially in road transportation systems as they enable improved navigation and tracking as well as offer resilience to wireless signals spoofing and jamming attacks. In this paper, we evaluate the security guarantees of INS-aided GPS tracking and navigation for road transportation systems. We consider an adversary required to travel from a source location to a destination and monitored by an INS-aided GPS system. The goal of the adversary is to travel to alternate locations without being detected. We develop and evaluate algorithms that achieve this goal, providing the adversary significant latitude. Our algorithms build a graph model for a given road network and enable us to derive potential destinations an attacker can reach without raising alarms even with the INS-aided GPS tracking and navigation system. The algorithms render the gyroscope and accelerometer sensors useless as they generate road trajectories indistinguishable from plausible paths (both in terms of turn angles and roads curvature). We also design, build and demonstrate that the magnetometer can be actively spoofed using a combination of carefully controlled coils. To experimentally demonstrate and evaluate the feasibility of the attack in real-world, we implement a first real-time integrated GPS/INS spoofer that accounts for traffic fluidity, congestion, lights, and dynamically generates corresponding spoofing signals. Furthermore, we evaluate our attack on ten different cities using driving traces and publicly available city plans. Our evaluations show that it is possible for an attacker to reach destinations that are as far as 30 km away from the actual destination without being detected. We also show that it is possible for the adversary to reach almost 60--80% of possible points within the target region in some cities. Such results are only a lower-bound, as an adversary can adjust our parameters to spend more resources (e.g., time) on the target source/destination than we did for our performance evaluations of thousands of paths. We propose countermeasures that limit an attacker's ability, without the need for any hardware modifications. Our system can be used as the foundation for countering such attacks, both detecting and recommending paths that are difficult to spoof.
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- PAR ID:
- 10132939
- Date Published:
- Journal Name:
- 2019 IEEE Symposium on Security and Privacy (SP)
- Volume:
- 1
- Page Range / eLocation ID:
- 1092-1106
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Conference Paper:
- https://doi.org/10.1109/SP.2019.00068
